
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''wavlm'''



     def __init__(				self							:							Optional[Any]     ,						_A							:							List[Any]=32     ,						_A							:							List[Any]=768     ,						_A							:							Optional[int]=12     ,						_A							:							Optional[int]=12     ,						_A							:							List[str]=3072     ,						_A							:							List[Any]="gelu"     ,						_A							:							Tuple=0.1     ,						_A							:							Dict=0.1     ,						_A							:							int=0.1     ,						_A							:							Optional[int]=0.0     ,						_A							:							Optional[int]=0.1     ,						_A							:							Any=0.1     ,						_A							:							List[Any]=0.02     ,						_A							:							Tuple=1e-5     ,						_A							:							Union[str, Any]="group"     ,						_A							:							Optional[Any]="gelu"     ,						_A							:							List[str]=(512, 512, 512, 512, 512, 512, 512)     ,						_A							:							List[str]=(5, 2, 2, 2, 2, 2, 2)     ,						_A							:							str=(10, 3, 3, 3, 3, 2, 2)     ,						_A							:							Union[str, Any]=False     ,						_A							:							Union[str, Any]=128     ,						_A							:							str=16     ,						_A							:							Union[str, Any]=320     ,						_A							:							Optional[int]=800     ,						_A							:							Dict=False     ,						_A							:							Optional[int]=True     ,						_A							:							Any=0.05     ,						_A							:							List[Any]=10     ,						_A							:							Tuple=2     ,						_A							:							Tuple=0.0     ,						_A							:							str=10     ,						_A							:							str=320     ,						_A							:							Any=2     ,						_A							:							Optional[Any]=0.1     ,						_A							:							Optional[int]=100     ,						_A							:							Union[str, Any]=256     ,						_A							:							Optional[Any]=256     ,						_A							:							Tuple=0.1     ,						_A							:							Union[str, Any]="mean"     ,						_A							:							str=False     ,						_A							:							Any=False     ,						_A							:							List[str]=256     ,						_A							:							str=(512, 512, 512, 512, 1500)     ,						_A							:							Any=(5, 3, 3, 1, 1)     ,						_A							:							List[str]=(1, 2, 3, 1, 1)     ,						_A							:							List[Any]=512     ,						_A							:							List[Any]=80     ,						_A							:							Optional[Any]=0     ,						_A							:							str=1     ,						_A							:							str=2     ,						_A							:							Union[str, Any]=False     ,						_A							:							List[str]=3     ,						_A							:							Any=2     ,						_A							:							str=3     ,						_A							:							Optional[Any]=None     ,						**_A							:							Any     ,						):







       """simple docstring"""




       super().__init__(**_A     ,						pad_token_id=_A     ,						bos_token_id=_A     ,						eos_token_id=_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		hidden_size
       __SCREAMING_SNAKE_CASE      :	int					=		feat_extract_norm
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		feat_extract_activation
       __SCREAMING_SNAKE_CASE      :	Dict					=		list(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		list(_A )
       __SCREAMING_SNAKE_CASE      :	Dict					=		list(_A )
       __SCREAMING_SNAKE_CASE      :	Dict					=		conv_bias
       __SCREAMING_SNAKE_CASE      :	int					=		num_buckets
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		max_bucket_distance
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		num_conv_pos_embeddings
       __SCREAMING_SNAKE_CASE      :	str					=		num_conv_pos_embedding_groups
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		len(self.conv_dim )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		num_hidden_layers
       __SCREAMING_SNAKE_CASE      :	int					=		intermediate_size
       __SCREAMING_SNAKE_CASE      :	Tuple					=		hidden_act
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		num_attention_heads
       __SCREAMING_SNAKE_CASE      :	str					=		hidden_dropout
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		attention_dropout
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		activation_dropout
       __SCREAMING_SNAKE_CASE      :	Any					=		feat_proj_dropout
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		final_dropout
       __SCREAMING_SNAKE_CASE      :	List[str]					=		layerdrop
       __SCREAMING_SNAKE_CASE      :	str					=		layer_norm_eps
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		initializer_range
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		num_ctc_classes
       __SCREAMING_SNAKE_CASE      :	Dict					=		vocab_size
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		do_stable_layer_norm
       __SCREAMING_SNAKE_CASE      :	str					=		use_weighted_layer_sum
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		classifier_proj_size

       if (
           (len(self.conv_stride ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel ) != self.num_feat_extract_layers)
           or (len(self.conv_dim ) != self.num_feat_extract_layers)
       ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
             F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       __SCREAMING_SNAKE_CASE      :	Tuple					=		apply_spec_augment
       __SCREAMING_SNAKE_CASE      :	Tuple					=		mask_time_prob
       __SCREAMING_SNAKE_CASE      :	int					=		mask_time_length
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		mask_time_min_masks
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		mask_feature_prob
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		mask_feature_length

       # parameters for pretraining with codevector quantized representations
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		num_codevectors_per_group
       __SCREAMING_SNAKE_CASE      :	Dict					=		num_codevector_groups
       __SCREAMING_SNAKE_CASE      :	Tuple					=		contrastive_logits_temperature
       __SCREAMING_SNAKE_CASE      :	int					=		num_negatives
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		codevector_dim
       __SCREAMING_SNAKE_CASE      :	List[str]					=		proj_codevector_dim
       __SCREAMING_SNAKE_CASE      :	Tuple					=		diversity_loss_weight

       # ctc loss
       __SCREAMING_SNAKE_CASE      :	str					=		ctc_loss_reduction
       __SCREAMING_SNAKE_CASE      :	Tuple					=		ctc_zero_infinity

       # adapter
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		add_adapter
       __SCREAMING_SNAKE_CASE      :	Dict					=		adapter_kernel_size
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		adapter_stride
       __SCREAMING_SNAKE_CASE      :	List[str]					=		num_adapter_layers
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		list(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		list(_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		list(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		xvector_output_dim




     @property
     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       return functools.reduce(operator.mul     ,						self.conv_stride     ,						1 )







"""simple docstring"""





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      lowerCAmelCase    		=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      i -= len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			i // 3
      lowerCAmelCase    		=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      lowerCAmelCase    		=			(
          chars_incl
          + random(SCREAMING_SNAKE_CASE				,		quotient + remainder     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      )
      lowerCAmelCase    		=			list(SCREAMING_SNAKE_CASE     )
      shuffle(SCREAMING_SNAKE_CASE     )
      return "".join(SCREAMING_SNAKE_CASE     )

      # random is a generalised function for letters, characters and numbers


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE     ) < min_length:
            # Your Password must be at least 8 characters long
            return False

      lowerCAmelCase    		=			any(char in ascii_uppercase for char in password     )
      lowerCAmelCase    		=			any(char in ascii_lowercase for char in password     )
      lowerCAmelCase    		=			any(char in digits for char in password     )
      lowerCAmelCase    		=			any(char in punctuation for char in password     )

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			int(input("""Please indicate the max length of your password: """     ).strip()     )
      lowerCAmelCase    		=			input(
          """Please indicate the characters that must be in your password: """     ).strip()
      print("""Password generated:"""				,		password_generator(SCREAMING_SNAKE_CASE     )     )
      print(
          """Alternative Password generated:"""				,		alternative_password_generator(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		)
      print("""[If you are thinking of using this passsword, You better save it.]"""     )


if __name__ == "__main__":
       main()






